{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test/Replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}23 Feb 2021, 11:50:36

{com}. do "/var/folders/g7/hp9p445x6kdbps08kwbb19gh0000gn/T//SD01403.000000"
{txt}
{com}.                                                                                                         *********************
. 
. //Gen dummy indicator for exppsure to Junqueras ruling in EU court//
. gen treatment=0
{txt}
{com}. replace treatment=1 if inwmms>=12 & inwyye==2019
{txt}(1,473 real changes made)

{com}. replace treatment=0 if inwmms==12 & inwdds<19
{txt}(1,170 real changes made)

{com}. replace treatment=1 if inwyye==2020
{txt}(478 real changes made)

{com}. 
. label define TREAT 0"Control" 1"Treatment"
{txt}
{com}. label var treatment "Exposure to Treatment"
{txt}
{com}. 
. gen votespain=.
{txt}(47,086 missing values generated)

{com}. replace votespain=1 if prtvtees==1
{txt}(187 real changes made)

{com}. replace votespain=2 if prtvtees==2
{txt}(313 real changes made)

{com}. replace votespain=3 if prtvtees==3
{txt}(129 real changes made)

{com}. replace votespain=4 if prtvtees==5
{txt}(68 real changes made)

{com}. replace votespain=3 if prtvtees==7
{txt}(7 real changes made)

{com}. replace votespain=5 if prtvtees==16
{txt}(104 real changes made)

{com}. 
. 
. label def SPAIN 1"PP" 2"PSOE" 3"UP" 4"Cs" 5"VOX"
{txt}
{com}. label value votespain SPAIN
{txt}
{com}. label var votespain "Vote choice in Spain"
{txt}
{com}. 
. gen voteconst=0
{txt}
{com}. replace voteconst=1 if votespain==1
{txt}(187 real changes made)

{com}. replace voteconst=1 if votespain==4
{txt}(68 real changes made)

{com}. replace voteconst=1 if votespain==5
{txt}(104 real changes made)

{com}. label def CONST 0"Voted for other party" 1"Voted for ''Constitional'' Party"
{txt}
{com}. label value voteconst CONST
{txt}
{com}. 
. recode euview (0=10 "Eurosceptic") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Europhile"),into(euview1)
{txt}(33538 differences between euview and euview1)

{com}. recode trstlgl (0=10 "Complete trust") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Complete distrust"),into(trustlegal)
{txt}(39156 differences between trstlgl and trustlegal)

{com}. 
. recode trstplc (0=10 "Complete trust") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Complete distrust"),into(trustpolice)
{txt}(40666 differences between trstplc and trustpolice)

{com}. recode trstplt (0=10 "Complete trust") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Complete distrust"),into(trustpolit)
{txt}(38315 differences between trstplt and trustpolit)

{com}. recode trstprt (0=10 "Complete trust") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Complete distrust"),into(trustpart)
{txt}(38191 differences between trstprt and trustpart)

{com}. recode trstep (0=10 "Complete trust") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Complete distrust"),into(trustep1)
{txt}(35251 differences between trstep and trustep1)

{com}. recode trstun (0=10 "Complete trust") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Complete distrust"),into(trstun1)
{txt}(34984 differences between trstun and trstun1)

{com}. recode stfgov (0=10 "Extremely satisfied") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Extremely dissatisfied"),into(disatgov)
{txt}(37533 differences between stfgov and disatgov)

{com}. recode swd (0=10 "Extremely satisfied") (1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1) (10=0 "Extremely dissatisfied"),into(diswd)
{txt}(37920 differences between swd and diswd)

{com}. 
. gen escep1=.
{txt}(47,086 missing values generated)

{com}. replace escep1=1 if euview1>5 & euview1!=.
{txt}(14,660 real changes made)

{com}. replace escep1=0 if euview1<=5 & euview1!=.
{txt}(28,681 real changes made)

{com}. 
. 
. egen edmean = median(education) if cntry=="ES"
{txt}(45,418 missing values generated)

{com}. gen education1=education
{txt}(553 missing values generated)

{com}. replace education1=edmean if education==.
{txt}(111 real changes made)

{com}. 
. label var education1 "Education with median imputation"
{txt}
{com}. 
. 
. replace lrscale=. if lrscale>10
{txt}(7,136 real changes made, 7,136 to missing)

{com}. egen lrmean = median(lrscale) if cntry=="ES"
{txt}(45,418 missing values generated)

{com}. gen lrscale1=lrscale
{txt}(7,136 missing values generated)

{com}. replace lrscale1=lrmean if lrscale==.
{txt}(223 real changes made)

{com}. 
. 
. label var lrscale1 "Left-right position with median imputation"
{txt}
{com}. 
. replace incomenew=. if incomenew>10
{txt}(9,373 real changes made, 9,373 to missing)

{com}. egen incomemean = median(incomenew) if cntry=="ES"
{txt}(45,418 missing values generated)

{com}. gen income1=incomenew
{txt}(9,373 missing values generated)

{com}. replace income1=incomemean if incomenew==.
{txt}(463 real changes made)

{com}. 
. label var education1 "Income with median imputation"
{txt}
{com}. 
. 
.                                                                                                         *********************
.                                                                                                         **     Analysis    **
.                                                                                                         *********************
. 
. 
. //Figure 3///
. 
.                 
. reg euview1 i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,483.03945732117)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(1, 1479)        =  {res}    10.36
                                                {txt}Prob > F          = {res}    0.0013
                                                {txt}R-squared         = {res}    0.0075
                                                {txt}Root MSE          =    {res} 2.5336

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4837643{col 26}{space 2} .1503062{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .1889283{col 67}{space 3} .7786003
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.655697{col 26}{space 2} .0778174{col 37}{space 1}   46.98{col 46}{space 3}0.000{col 54}{space 4} 3.503053{col 67}{space 3} 3.808342
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store it1
{txt}
{com}. 
. reg euview1 i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,483.03945732117)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(14, 1466)       =  {res}     8.17
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0824
                                                {txt}Root MSE          =    {res} 2.4469

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4673591{col 26}{space 2} .1468383{col 37}{space 1}    3.18{col 46}{space 3}0.001{col 54}{space 4} .1793236{col 67}{space 3} .7553947
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.3394577{col 26}{space 2} .1299738{col 37}{space 1}   -2.61{col 46}{space 3}0.009{col 54}{space 4}-.5944121{col 67}{space 3}-.0845033
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4515842{col 26}{space 2} .2473679{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0336486{col 67}{space 3}  .936817
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5123816{col 26}{space 2} .2356077{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0502173{col 67}{space 3} .9745458
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3174599{col 26}{space 2} .2254044{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.1246896{col 67}{space 3} .7596094
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1134078{col 26}{space 2} .2310335{col 37}{space 1}    0.49{col 46}{space 3}0.624{col 54}{space 4}-.3397838{col 67}{space 3} .5665993
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2027136{col 26}{space 2} .2602598{col 37}{space 1}    0.78{col 46}{space 3}0.436{col 54}{space 4}-.3078077{col 67}{space 3} .7132349
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .6239109{col 26}{space 2} .2993889{col 37}{space 1}    2.08{col 46}{space 3}0.037{col 54}{space 4} .0366345{col 67}{space 3} 1.211187
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2068665{col 26}{space 2}    .2396{col 37}{space 1}   -0.86{col 46}{space 3}0.388{col 54}{space 4} -.676862{col 67}{space 3}  .263129
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5576301{col 26}{space 2} .2358931{col 37}{space 1}   -2.36{col 46}{space 3}0.018{col 54}{space 4}-1.020354{col 67}{space 3} -.094906
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.9221003{col 26}{space 2} .3059081{col 37}{space 1}   -3.01{col 46}{space 3}0.003{col 54}{space 4}-1.522165{col 67}{space 3} -.322036
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-1.177681{col 26}{space 2} .2372037{col 37}{space 1}   -4.96{col 46}{space 3}0.000{col 54}{space 4}-1.642975{col 67}{space 3}-.7123858
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2}-.0341645{col 26}{space 2} .0327503{col 37}{space 1}   -1.04{col 46}{space 3}0.297{col 54}{space 4} -.098407{col 67}{space 3}  .030078
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2}  .197989{col 26}{space 2} .0344981{col 37}{space 1}    5.74{col 46}{space 3}0.000{col 54}{space 4}  .130318{col 67}{space 3}   .26566
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.395204{col 26}{space 2} .3383878{col 37}{space 1}   10.03{col 46}{space 3}0.000{col 54}{space 4} 2.731428{col 67}{space 3}  4.05898
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store it2
{txt}
{com}. 
. 
. logit escep1 i.treatment [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-715.94249}  
Iteration 1:{space 3}log pseudolikelihood = {res:-712.45498}  
Iteration 2:{space 3}log pseudolikelihood = {res:-712.43707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-712.43707}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,481
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      6.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0085
{txt}Log pseudolikelihood = {res}-712.43707{txt}{col 49}Pseudo R2{col 67}= {res}    0.0049

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      escep1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .3757574{col 26}{space 2} .1427302{col 37}{space 1}    2.63{col 46}{space 3}0.008{col 54}{space 4} .0960114{col 67}{space 3} .6555035
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.585629{col 26}{space 2} .0831663{col 37}{space 1}  -19.07{col 46}{space 3}0.000{col 54}{space 4}-1.748632{col 67}{space 3}-1.422626
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     1,481
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(escep1), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .059724{col 26}{space 2} .0236434{col 37}{space 1}    2.53{col 46}{space 3}0.012{col 54}{space 4} .0133838{col 67}{space 3} .1060642
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store it10
{txt}
{com}. 
. logit escep1 i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-715.94249}  
Iteration 1:{space 3}log pseudolikelihood = {res: -678.9784}  
Iteration 2:{space 3}log pseudolikelihood = {res:-677.53467}  
Iteration 3:{space 3}log pseudolikelihood = {res:-677.52966}  
Iteration 4:{space 3}log pseudolikelihood = {res:-677.52966}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,481
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}     65.63
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-677.52966{txt}{col 49}Pseudo R2{col 67}= {res}    0.0537

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      escep1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4095346{col 26}{space 2} .1467409{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .1219276{col 67}{space 3} .6971416
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.0248806{col 26}{space 2} .1408978{col 37}{space 1}   -0.18{col 46}{space 3}0.860{col 54}{space 4}-.3010354{col 67}{space 3} .2512741
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .8438959{col 26}{space 2} .2974902{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .2608259{col 67}{space 3} 1.426966
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6134535{col 26}{space 2} .2804496{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .0637825{col 67}{space 3} 1.163125
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .6249733{col 26}{space 2} .2731928{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0895252{col 67}{space 3} 1.160421
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3556504{col 26}{space 2} .2878176{col 37}{space 1}    1.24{col 46}{space 3}0.217{col 54}{space 4}-.2084617{col 67}{space 3} .9197625
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6382227{col 26}{space 2} .3034233{col 37}{space 1}    2.10{col 46}{space 3}0.035{col 54}{space 4}  .043524{col 67}{space 3} 1.232921
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .8473957{col 26}{space 2}  .326202{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .2080516{col 67}{space 3}  1.48674
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.0965314{col 26}{space 2} .2060482{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.5003783{col 67}{space 3} .3073156
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4229874{col 26}{space 2} .2216495{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.8574125{col 67}{space 3} .0114377
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.7779698{col 26}{space 2} .3391263{col 37}{space 1}   -2.29{col 46}{space 3}0.022{col 54}{space 4}-1.442645{col 67}{space 3}-.1132945
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-1.083323{col 26}{space 2} .2538099{col 37}{space 1}   -4.27{col 46}{space 3}0.000{col 54}{space 4}-1.580782{col 67}{space 3}-.5858651
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} -.035514{col 26}{space 2} .0368136{col 37}{space 1}   -0.96{col 46}{space 3}0.335{col 54}{space 4}-.1076674{col 67}{space 3} .0366394
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2} .1259115{col 26}{space 2}  .035194{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .0569325{col 67}{space 3} .1948905
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.134995{col 26}{space 2} .3708537{col 37}{space 1}   -5.76{col 46}{space 3}0.000{col 54}{space 4}-2.861855{col 67}{space 3}-1.408135
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,481
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(escep1), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment 1.gender 1.agecat 2.agecat 3.agecat 4.agecat 5.agecat 6.agecat 2.education1 3.education1 4.education1 5.education1 income1 lrscale1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .0619704{col 26}{space 2} .0230264{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4} .0168395{col 67}{space 3} .1071013
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.0035972{col 26}{space 2}  .020368{col 37}{space 1}   -0.18{col 46}{space 3}0.860{col 54}{space 4}-.0435177{col 67}{space 3} .0363233
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1138961{col 26}{space 2} .0394732{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4} .0365299{col 67}{space 3} .1912622
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0770199{col 26}{space 2} .0336984{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0109722{col 67}{space 3} .1430675
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0787588{col 26}{space 2} .0326311{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0148031{col 67}{space 3} .1427146
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0409785{col 26}{space 2} .0324041{col 37}{space 1}    1.26{col 46}{space 3}0.206{col 54}{space 4}-.0225324{col 67}{space 3} .1044895
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0807726{col 26}{space 2} .0385192{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0052763{col 67}{space 3} .1562688
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1144897{col 26}{space 2} .0459876{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} .0243556{col 67}{space 3} .2046238
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.0174547{col 26}{space 2} .0375398{col 37}{space 1}   -0.46{col 46}{space 3}0.642{col 54}{space 4}-.0910314{col 67}{space 3}  .056122
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0704746{col 26}{space 2} .0376199{col 37}{space 1}   -1.87{col 46}{space 3}0.061{col 54}{space 4}-.1442083{col 67}{space 3}  .003259
{txt}{space 10}4  {c |}{col 14}{res}{space 2} -.117592{col 26}{space 2} .0465915{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.2089098{col 67}{space 3}-.0262743
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1499174{col 26}{space 2}  .036562{col 37}{space 1}   -4.10{col 46}{space 3}0.000{col 54}{space 4}-.2215775{col 67}{space 3}-.0782573
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} -.005133{col 26}{space 2} .0053258{col 37}{space 1}   -0.96{col 46}{space 3}0.335{col 54}{space 4}-.0155713{col 67}{space 3} .0053054
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2} .0181984{col 26}{space 2} .0050428{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} .0083148{col 67}{space 3}  .028082
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store it20
{txt}
{com}. 
. coefplot (it1, mlabel("β=.48 | t=3.22 | p=.0001") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (it2, mlabel("β=.47 | t=3.18 | p=.001") mlabposition(2) msymbol(diamond) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))), keep(*.treatment) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50)) ylabel("") xtitle("") title("Effect on euroscepticism (0-10)") ///
>                 legend(off) levels(95 90) 
{res}{txt}
{com}.                 graph save itt1.gph, replace
{res}{txt}(file itt1.gph saved)

{com}. 
. 
. 
. coefplot (it10, mlabel("β=5.972 | z=2.53 | p=.012") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (it20, mlabel("β=6.197 | z=2.69 | p=.007") mlabposition(2) msymbol(diamond) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))), keep(*.treatment) rescale(100) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50)) ylabel("") xtitle("") title("Effect on Pr(Eurosceptic)") ///
>                 legend(position(12) row(1) size(vsmall)) levels(95 90) 
{res}{txt}
{com}.                         graph save itt2.gph, replace
{res}{txt}(file itt2.gph saved)

{com}. 
.                         grc1leg itt1.gph itt2.gph, legendfrom(itt2.gph) row(1) note("Confidence intervals at 95% and 90%", size(small) position(5)) title("Treatment effects on Euroscepticism") 
{res}{txt}
{com}. 
. 
. 
. 
. //Figure 4///
.                 
.                 
. gen dissatisfied=.
{txt}(47,086 missing values generated)

{com}. replace dissatisfied=1 if diswd>5 & diswd!=.
{txt}(16,494 real changes made)

{com}. replace dissatisfied=0 if diswd<=5 & diswd!=.
{txt}(28,808 real changes made)

{com}. label def DISAT 0 "Satisfied with democracy" 1"Dissatisfied with democracy"
{txt}
{com}. label value dissatisfied DISAT
{txt}
{com}. label var dissatisfied "Dissatisfied with democracy"
{txt}
{com}. 
. reg diswd i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,584.48301851749)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(1, 1582)        =  {res}     5.32
                                                {txt}Prob > F          = {res}    0.0212
                                                {txt}R-squared         = {res}    0.0035
                                                {txt}Root MSE          =    {res} 2.4794

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .321891{col 26}{space 2} .1395171{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0482333{col 67}{space 3} .5955488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.123155{col 26}{space 2} .0734326{col 37}{space 1}   69.77{col 46}{space 3}0.000{col 54}{space 4}  4.97912{col 67}{space 3} 5.267191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store ita1
{txt}
{com}. 
. reg diswd i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,584.48301851749)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(14, 1569)       =  {res}     3.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0295
                                                {txt}Root MSE          =    {res} 2.4569

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .3013953{col 26}{space 2} .1389248{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0288974{col 67}{space 3} .5738932
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.2237634{col 26}{space 2} .1266658{col 37}{space 1}   -1.77{col 46}{space 3}0.077{col 54}{space 4}-.4722155{col 67}{space 3} .0246887
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4876092{col 26}{space 2} .2405177{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0158393{col 67}{space 3}  .959379
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4712224{col 26}{space 2}  .227242{col 37}{space 1}    2.07{col 46}{space 3}0.038{col 54}{space 4} .0254925{col 67}{space 3} .9169523
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0465388{col 26}{space 2} .2166379{col 37}{space 1}    0.21{col 46}{space 3}0.830{col 54}{space 4}-.3783916{col 67}{space 3} .4714692
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0796382{col 26}{space 2} .2202759{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.5117043{col 67}{space 3} .3524279
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1016798{col 26}{space 2} .2414978{col 37}{space 1}   -0.42{col 46}{space 3}0.674{col 54}{space 4}-.5753722{col 67}{space 3} .3720126
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .199697{col 26}{space 2} .2803969{col 37}{space 1}    0.71{col 46}{space 3}0.476{col 54}{space 4} -.350295{col 67}{space 3} .7496891
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3771882{col 26}{space 2} .2116384{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0379357{col 67}{space 3} .7923121
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0796793{col 26}{space 2} .2076846{col 37}{space 1}   -0.38{col 46}{space 3}0.701{col 54}{space 4} -.487048{col 67}{space 3} .3276893
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4253169{col 26}{space 2} .3287269{col 37}{space 1}    1.29{col 46}{space 3}0.196{col 54}{space 4}-.2194734{col 67}{space 3} 1.070107
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1348748{col 26}{space 2} .2136237{col 37}{space 1}    0.63{col 46}{space 3}0.528{col 54}{space 4}-.2841432{col 67}{space 3} .5538928
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} .0467766{col 26}{space 2} .0303072{col 37}{space 1}    1.54{col 46}{space 3}0.123{col 54}{space 4}-.0126703{col 67}{space 3} .1062235
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2}-.0993283{col 26}{space 2} .0320461{col 37}{space 1}   -3.10{col 46}{space 3}0.002{col 54}{space 4} -.162186{col 67}{space 3}-.0364707
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.156416{col 26}{space 2} .3137222{col 37}{space 1}   16.44{col 46}{space 3}0.000{col 54}{space 4} 4.541057{col 67}{space 3} 5.771774
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store ita2
{txt}
{com}. 
. logit dissatisfied i.treatment [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1063.1548}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1060.8562}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1060.8558}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1060.8558}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,584
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      4.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0333
{txt}Log pseudolikelihood = {res}-1060.8558{txt}{col 49}Pseudo R2{col 67}= {res}    0.0022

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfied{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .241286{col 26}{space 2} .1133648{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0190952{col 67}{space 3} .4634768
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4972156{col 26}{space 2} .0619604{col 37}{space 1}   -8.02{col 46}{space 3}0.000{col 54}{space 4}-.6186557{col 67}{space 3}-.3757756
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     1,584
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(dissatisfied), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .0581693{col 26}{space 2} .0275225{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0042262{col 67}{space 3} .1121125
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store ita10
{txt}
{com}. 
. logit dissatisfied i.treatment i.gender i.agecat i.education1 c.income1  c.lrscale1 [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1063.1548}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1036.1887}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1036.1157}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1036.1157}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,584
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}     50.49
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1036.1157{txt}{col 49}Pseudo R2{col 67}= {res}    0.0254

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfied{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .2377994{col 26}{space 2} .1168194{col 37}{space 1}    2.04{col 46}{space 3}0.042{col 54}{space 4} .0088375{col 67}{space 3} .4667613
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.1640516{col 26}{space 2} .1069507{col 37}{space 1}   -1.53{col 46}{space 3}0.125{col 54}{space 4}-.3736712{col 67}{space 3}  .045568
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4948253{col 26}{space 2} .2118694{col 37}{space 1}    2.34{col 46}{space 3}0.020{col 54}{space 4}  .079569{col 67}{space 3} .9100817
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3561565{col 26}{space 2} .1960263{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0280479{col 67}{space 3} .7403609
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0725169{col 26}{space 2} .1937496{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.4522591{col 67}{space 3} .3072252
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0268763{col 26}{space 2} .2012114{col 37}{space 1}   -0.13{col 46}{space 3}0.894{col 54}{space 4}-.4212433{col 67}{space 3} .3674907
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0816712{col 26}{space 2} .2227435{col 37}{space 1}    0.37{col 46}{space 3}0.714{col 54}{space 4}-.3548981{col 67}{space 3} .5182405
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1728131{col 26}{space 2} .2453533{col 37}{space 1}    0.70{col 46}{space 3}0.481{col 54}{space 4}-.3080705{col 67}{space 3} .6536967
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .4809008{col 26}{space 2} .1847601{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .1187777{col 67}{space 3} .8430239
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .228298{col 26}{space 2} .1852988{col 37}{space 1}    1.23{col 46}{space 3}0.218{col 54}{space 4}-.1348811{col 67}{space 3}  .591477
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4650678{col 26}{space 2} .2679641{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0601323{col 67}{space 3} .9902678
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4481256{col 26}{space 2} .1886632{col 37}{space 1}    2.38{col 46}{space 3}0.018{col 54}{space 4} .0783524{col 67}{space 3} .8178987
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} .0378962{col 26}{space 2} .0262028{col 37}{space 1}    1.45{col 46}{space 3}0.148{col 54}{space 4}-.0134604{col 67}{space 3} .0892528
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2} -.090632{col 26}{space 2} .0264598{col 37}{space 1}   -3.43{col 46}{space 3}0.001{col 54}{space 4}-.1424922{col 67}{space 3}-.0387718
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6815835{col 26}{space 2} .2803286{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-1.231017{col 67}{space 3}-.1321495
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,584
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(dissatisfied), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment 1.gender 1.agecat 2.agecat 3.agecat 4.agecat 5.agecat 6.agecat 2.education1 3.education1 4.education1 5.education1 income1 lrscale1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .0555185{col 26}{space 2} .0274657{col 37}{space 1}    2.02{col 46}{space 3}0.043{col 54}{space 4} .0016867{col 67}{space 3} .1093502
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.0379244{col 26}{space 2} .0247087{col 37}{space 1}   -1.53{col 46}{space 3}0.125{col 54}{space 4}-.0863526{col 67}{space 3} .0105038
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1172409{col 26}{space 2} .0497649{col 37}{space 1}    2.36{col 46}{space 3}0.018{col 54}{space 4} .0197034{col 67}{space 3} .2147783
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0836069{col 26}{space 2} .0455668{col 37}{space 1}    1.83{col 46}{space 3}0.067{col 54}{space 4}-.0057024{col 67}{space 3} .1729162
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0162426{col 26}{space 2} .0434874{col 37}{space 1}   -0.37{col 46}{space 3}0.709{col 54}{space 4}-.1014763{col 67}{space 3}  .068991
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0060576{col 26}{space 2}  .045374{col 37}{space 1}   -0.13{col 46}{space 3}0.894{col 54}{space 4}-.0949891{col 67}{space 3} .0828738
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0186618{col 26}{space 2} .0508962{col 37}{space 1}    0.37{col 46}{space 3}0.714{col 54}{space 4}-.0810929{col 67}{space 3} .1184166
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0398927{col 26}{space 2}  .056768{col 37}{space 1}    0.70{col 46}{space 3}0.482{col 54}{space 4}-.0713706{col 67}{space 3} .1511559
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .1093189{col 26}{space 2} .0409591{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0290406{col 67}{space 3} .1895972
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0502847{col 26}{space 2} .0403992{col 37}{space 1}    1.24{col 46}{space 3}0.213{col 54}{space 4}-.0288963{col 67}{space 3} .1294657
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1055401{col 26}{space 2} .0617894{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0155649{col 67}{space 3} .2266452
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1015063{col 26}{space 2} .0419152{col 37}{space 1}    2.42{col 46}{space 3}0.015{col 54}{space 4}  .019354{col 67}{space 3} .1836585
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} .0087517{col 26}{space 2} .0060328{col 37}{space 1}    1.45{col 46}{space 3}0.147{col 54}{space 4}-.0030724{col 67}{space 3} .0205757
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2}-.0209304{col 26}{space 2}  .006009{col 37}{space 1}   -3.48{col 46}{space 3}0.000{col 54}{space 4}-.0327079{col 67}{space 3}-.0091528
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store ita20
{txt}
{com}. 
. coefplot (ita1, mlabel("β=.32 | t=2.31 | p=.021") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (ita2, mlabel("β=.30 | t=2.17 | p=.030") mlabposition(2) msymbol(diamond) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))), keep(*.treatment) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50)) ylabel("") xtitle("") title("Effect on dissatisfaction with democracy (0-10)") ///
>                 legend(off) levels(95 90) 
{res}{txt}
{com}.                 graph save itta1.gph, replace
{res}{txt}(file itta1.gph saved)

{com}. 
. 
. 
. coefplot (ita10, mlabel("β=5.82 | z=2.11| p=.04") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (ita20, mlabel("β=5.55 | z=2.02| p=.043") mlabposition(2) msymbol(diamond) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))), keep(*.treatment) rescale(100) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50)) ylabel("") xtitle("") title("Effect on Pr(Dissatsified with democracy)") ///
>                 legend(position(12) row(1) size(vsmall)) levels(95 90) 
{res}{txt}
{com}.                         graph save itta2.gph, replace
{res}{txt}(file itta2.gph saved)

{com}. 
.                         grc1leg itta1.gph itta2.gph, legendfrom(itta2.gph) row(1) note("Confidence intervals at 95% and 90%", size(small) position(5)) title("Treatment effects on Dissatisfaction with democracy")
{res}{txt}
{com}. 
. 
. 
. 
. ///Figure 6///
> 
. reg euview1 i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,483.03945732117)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(1, 1479)        =  {res}    10.36
                                                {txt}Prob > F          = {res}    0.0013
                                                {txt}R-squared         = {res}    0.0075
                                                {txt}Root MSE          =    {res} 2.5336

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4837643{col 26}{space 2} .1503062{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .1889283{col 67}{space 3} .7786003
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.655697{col 26}{space 2} .0778174{col 37}{space 1}   46.98{col 46}{space 3}0.000{col 54}{space 4} 3.503053{col 67}{space 3} 3.808342
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store b1
{txt}
{com}. 
. reg euview1 i.treatment [pweight=dweight] if cntry=="HR", robust
{txt}(sum of wgt is 1,636.75609282906)

Linear regression                               Number of obs     = {res}     1,623
                                                {txt}F(1, 1621)        =  {res}     0.40
                                                {txt}Prob > F          = {res}    0.5250
                                                {txt}R-squared         = {res}    0.0003
                                                {txt}Root MSE          =    {res} 2.7984

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .1690411{col 26}{space 2} .2658696{col 37}{space 1}    0.64{col 46}{space 3}0.525{col 54}{space 4}-.3524431{col 67}{space 3} .6905253
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.767584{col 26}{space 2} .0844046{col 37}{space 1}   56.48{col 46}{space 3}0.000{col 54}{space 4}  4.60203{col 67}{space 3} 4.933137
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store b2
{txt}
{com}. 
. reg euview1 i.treatment [pweight=dweight] if cntry=="LV", robust
{txt}(sum of wgt is 759.0744013786316)

Linear regression                               Number of obs     = {res}       757
                                                {txt}F(1, 755)         =  {res}     0.19
                                                {txt}Prob > F          = {res}    0.6654
                                                {txt}R-squared         = {res}    0.0003
                                                {txt}Root MSE          =    {res} 2.8796

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}-.1346646{col 26}{space 2} .3112967{col 37}{space 1}   -0.43{col 46}{space 3}0.665{col 54}{space 4}-.7457747{col 67}{space 3} .4764454
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.176608{col 26}{space 2} .1219478{col 37}{space 1}   42.45{col 46}{space 3}0.000{col 54}{space 4} 4.937211{col 67}{space 3} 5.416005
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store b3
{txt}
{com}. 
. coefplot (b1, mlabel("β=.48 | t=3.22 | p=.0001") mlabposition(6) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (b2, mlabel("β=.17 | t=.64 | p=.525") mlabposition(6) msymbol(square) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))) (b3, mlabel("β=-.13 | t=-.43| p=.665") mlabposition(6) msymbol(triangle) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))), keep(*.treatment) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50) ) ylabel("") xtitle("ITT of ECJ ruling") title("(a) Euroscepticism") ///
>                 legend(off) levels(95 90)
{res}{txt}
{com}. 
.                 graph save placebo2a.gph, replace
{res}{txt}(file placebo2a.gph saved)

{com}. 
. reg diswd  i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,584.48301851749)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(1, 1582)        =  {res}     5.32
                                                {txt}Prob > F          = {res}    0.0212
                                                {txt}R-squared         = {res}    0.0035
                                                {txt}Root MSE          =    {res} 2.4794

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .321891{col 26}{space 2} .1395171{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0482333{col 67}{space 3} .5955488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.123155{col 26}{space 2} .0734326{col 37}{space 1}   69.77{col 46}{space 3}0.000{col 54}{space 4}  4.97912{col 67}{space 3} 5.267191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store s1
{txt}
{com}. 
. reg diswd  i.treatment [pweight=dweight] if cntry=="HR", robust
{txt}(sum of wgt is 1,767.70036140674)

Linear regression                               Number of obs     = {res}     1,761
                                                {txt}F(1, 1759)        =  {res}     0.03
                                                {txt}Prob > F          = {res}    0.8604
                                                {txt}R-squared         = {res}    0.0000
                                                {txt}Root MSE          =    {res}  2.332

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}-.0416307{col 26}{space 2} .2367444{col 37}{space 1}   -0.18{col 46}{space 3}0.860{col 54}{space 4}-.5059608{col 67}{space 3} .4226993
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 6.722015{col 26}{space 2} .0660966{col 37}{space 1}  101.70{col 46}{space 3}0.000{col 54}{space 4} 6.592378{col 67}{space 3} 6.851651
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store s2
{txt}
{com}. 
. reg diswd  i.treatment [pweight=dweight] if cntry=="LV", robust
{txt}(sum of wgt is 822.0445756316185)

Linear regression                               Number of obs     = {res}       809
                                                {txt}F(1, 807)         =  {res}     0.18
                                                {txt}Prob > F          = {res}    0.6702
                                                {txt}R-squared         = {res}    0.0003
                                                {txt}Root MSE          =    {res} 2.5926

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}-.1270091{col 26}{space 2} .2980761{col 37}{space 1}   -0.43{col 46}{space 3}0.670{col 54}{space 4} -.712105{col 67}{space 3} .4580869
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  5.68948{col 26}{space 2} .1036066{col 37}{space 1}   54.91{col 46}{space 3}0.000{col 54}{space 4}  5.48611{col 67}{space 3} 5.892851
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store s3
{txt}
{com}. 
. coefplot (s1, mlabel("β=.32 | t=2.31 | p=.021") mlabposition(6) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (s2, mlabel("β=-.042 | t=-.18 | p=.860") mlabposition(6) msymbol(square) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))) (s3, mlabel("β=-.128 | t=-.43| p=.670") mlabposition(6) msymbol(triangle) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))), keep(*.treatment) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50) ) ylabel("") xtitle("ITT of ECJ ruling") title("(b) Dissatisfaction with democracy") ///
>                 legend(off) levels(95 90)
{res}{txt}
{com}. 
.                 graph save placebo2b.gph, replace
{res}{txt}(file placebo2b.gph saved)

{com}. reg trustlegal i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,634.71978598833)

Linear regression                               Number of obs     = {res}     1,635
                                                {txt}F(1, 1633)        =  {res}     4.31
                                                {txt}Prob > F          = {res}    0.0381
                                                {txt}R-squared         = {res}    0.0027
                                                {txt}Root MSE          =    {res} 2.6621

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  trustlegal{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .3054667{col 26}{space 2} .1471974{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} .0167512{col 67}{space 3} .5941822
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.132383{col 26}{space 2} .0777604{col 37}{space 1}   66.00{col 46}{space 3}0.000{col 54}{space 4} 4.979862{col 67}{space 3} 5.284904
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store c1
{txt}
{com}. 
. reg trustlegal i.treatment [pweight=dweight] if cntry=="HR", robust
{txt}(sum of wgt is 1,790.20098879771)

Linear regression                               Number of obs     = {res}     1,791
                                                {txt}F(1, 1789)        =  {res}     0.01
                                                {txt}Prob > F          = {res}    0.9179
                                                {txt}R-squared         = {res}    0.0000
                                                {txt}Root MSE          =    {res} 2.2949

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  trustlegal{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}-.0224686{col 26}{space 2} .2178377{col 37}{space 1}   -0.10{col 46}{space 3}0.918{col 54}{space 4}-.4497117{col 67}{space 3} .4047744
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 7.488989{col 26}{space 2} .0632381{col 37}{space 1}  118.43{col 46}{space 3}0.000{col 54}{space 4} 7.364961{col 67}{space 3} 7.613018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store c2
{txt}
{com}. 
. reg trustlegal i.treatment [pweight=dweight] if cntry=="LV", robust
{txt}(sum of wgt is 858.5546234250069)

Linear regression                               Number of obs     = {res}       852
                                                {txt}F(1, 850)         =  {res}     1.01
                                                {txt}Prob > F          = {res}    0.3153
                                                {txt}R-squared         = {res}    0.0013
                                                {txt}Root MSE          =    {res} 2.6655

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  trustlegal{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .2811934{col 26}{space 2} .2798457{col 37}{space 1}    1.00{col 46}{space 3}0.315{col 54}{space 4}-.2680763{col 67}{space 3} .8304631
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.575703{col 26}{space 2} .1081499{col 37}{space 1}   51.56{col 46}{space 3}0.000{col 54}{space 4} 5.363431{col 67}{space 3} 5.787975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store c3
{txt}
{com}. 
. 
. coefplot (c1, mlabel("β=.31 | t=2.08 | p=.038") mlabposition(6) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (c2, mlabel("β=-.02 | t=-.10 | p=.918") mlabposition(6) msymbol(square) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))) (c3, mlabel("β=.28 | t=1.00 | p=..315") mlabposition(6) msymbol(triangle) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))), keep(*.treatment) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50) ) ylabel("") xtitle("ITT of ECJ ruling") title("(c) Distrust in legal system") ///
>                 legend(position(6) row(1) size(vsmall)) levels(95 90)
{res}{txt}
{com}. 
.                                         graph save placebo2c.gph, replace       
{res}{txt}(file placebo2c.gph saved)

{com}.                                         
.                                 grc1leg placebo2a.gph placebo2b.gph placebo2c.gph, legendfrom(placebo2c.gph) row(1) title("Placebo test (ii): Countries")  note("Confidence intervals at 95% and 90%", size(vsmall) position(5))
{res}{txt}
{com}. 
. 
.                 
. //Figure 5///
. reg euview1 i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,483.03945732117)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(1, 1479)        =  {res}    10.36
                                                {txt}Prob > F          = {res}    0.0013
                                                {txt}R-squared         = {res}    0.0075
                                                {txt}Root MSE          =    {res} 2.5336

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4837643{col 26}{space 2} .1503062{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .1889283{col 67}{space 3} .7786003
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.655697{col 26}{space 2} .0778174{col 37}{space 1}   46.98{col 46}{space 3}0.000{col 54}{space 4} 3.503053{col 67}{space 3} 3.808342
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store t1
{txt}
{com}. 
. reg diswd i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,584.48301851749)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(1, 1582)        =  {res}     5.32
                                                {txt}Prob > F          = {res}    0.0212
                                                {txt}R-squared         = {res}    0.0035
                                                {txt}Root MSE          =    {res} 2.4794

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .321891{col 26}{space 2} .1395171{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0482333{col 67}{space 3} .5955488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.123155{col 26}{space 2} .0734326{col 37}{space 1}   69.77{col 46}{space 3}0.000{col 54}{space 4}  4.97912{col 67}{space 3} 5.267191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store t0
{txt}
{com}. 
. reg trustlegal i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,634.71978598833)

Linear regression                               Number of obs     = {res}     1,635
                                                {txt}F(1, 1633)        =  {res}     4.31
                                                {txt}Prob > F          = {res}    0.0381
                                                {txt}R-squared         = {res}    0.0027
                                                {txt}Root MSE          =    {res} 2.6621

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  trustlegal{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .3054667{col 26}{space 2} .1471974{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} .0167512{col 67}{space 3} .5941822
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.132383{col 26}{space 2} .0777604{col 37}{space 1}   66.00{col 46}{space 3}0.000{col 54}{space 4} 4.979862{col 67}{space 3} 5.284904
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store t2
{txt}
{com}. 
. reg immigrationview i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,581.95428878069)

Linear regression                               Number of obs     = {res}     1,581
                                                {txt}F(1, 1579)        =  {res}     0.01
                                                {txt}Prob > F          = {res}    0.9110
                                                {txt}R-squared         = {res}    0.0000
                                                {txt}Root MSE          =    {res} 2.4869

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}immigratio~w{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}-.0153556{col 26}{space 2} .1372913{col 37}{space 1}   -0.11{col 46}{space 3}0.911{col 54}{space 4} -.284648{col 67}{space 3} .2539368
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  5.85869{col 26}{space 2}  .075377{col 37}{space 1}   77.73{col 46}{space 3}0.000{col 54}{space 4}  5.71084{col 67}{space 3}  6.00654
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store t3
{txt}
{com}. 
. reg lrscale i.treatment [pweight=dweight]if cntry=="ES", robust
{txt}(sum of wgt is 1,441.64530485868)

Linear regression                               Number of obs     = {res}     1,445
                                                {txt}F(1, 1443)        =  {res}     0.02
                                                {txt}Prob > F          = {res}    0.8829
                                                {txt}R-squared         = {res}    0.0000
                                                {txt}Root MSE          =    {res} 2.2645

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     lrscale{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .0186973{col 26}{space 2} .1269555{col 37}{space 1}    0.15{col 46}{space 3}0.883{col 54}{space 4}-.2303398{col 67}{space 3} .2677345
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.439752{col 26}{space 2} .0727385{col 37}{space 1}   61.04{col 46}{space 3}0.000{col 54}{space 4} 4.297068{col 67}{space 3} 4.582437
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store t4
{txt}
{com}. 
. reg trstun1 i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,444.40946346521)

Linear regression                               Number of obs     = {res}     1,446
                                                {txt}F(1, 1444)        =  {res}     0.30
                                                {txt}Prob > F          = {res}    0.5843
                                                {txt}R-squared         = {res}    0.0002
                                                {txt}Root MSE          =    {res} 2.5643

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     trstun1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}-.0812488{col 26}{space 2} .1484783{col 37}{space 1}   -0.55{col 46}{space 3}0.584{col 54}{space 4}-.3725051{col 67}{space 3} .2100075
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.026494{col 26}{space 2} .0807634{col 37}{space 1}   62.24{col 46}{space 3}0.000{col 54}{space 4} 4.868068{col 67}{space 3}  5.18492
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store t5
{txt}
{com}.                 
. coefplot (t1, mlabel("β=.48 | t=3.22 | p=.0001") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (t0, mlabel("β=.32 | t=2.31 | p=.021") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue)))(t2, mlabel("β=.31 | t=2.08 | p=.038") mlabposition(2) msymbol(circle) ///
> msize(small) mlabcolor(blue) mcolor(blue) ciopts(recast(. rcap) color(blue))) (t5, mlabel("β=.-08 | t=-.55| p=.584") mlabposition(2) msymbol(triangle) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black)))(t3, mlabel("β=-.015 | t=-.11 | p=.911") mlabposition(2) msymbol(square) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))) (t4, mlabel("β=.018 | t=.15 | p=.883") mlabposition(2) msymbol(diamond) ///
> msize(small) mlabcolor(black) mcolor(black) ciopts(recast(. rcap) color(black))), keep(*.treatment) xline(0, lpattern(dash) lwidth(thick) lcolor(cranberry%50) ) ylabel("") xtitle("ITT effect") title("Placebo test (i): Issues") ///
>                 legend(position(9) size(vsmall) col(1)) levels(95 90) note("Confidence intervals at 95% and 90%", size(vsmall) position(5)) plotlabels("Euroscepticism" "Dissatisfaction with democracy" "Disrust in legal system" "Distrust in UN" "Immigration attitudes" "Left-right position")
{res}{txt}
{com}. 
. graph save placebo_issues.gph, replace
{res}{txt}(file placebo_issues.gph saved)

{com}. 
. 
. ///Figure 7///
> 
. ritest treatment _b[treatment], reps(1000) kdensityplot: reg euview1 treatment if cntry=="ES", robust
{res}{txt}(running {bf:regress} on estimation sample)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(1, 1479)        =  {res}     9.82
                                                {txt}Prob > F          = {res}    0.0018
                                                {txt}R-squared         = {res}    0.0069
                                                {txt}Root MSE          =    {res} 2.5201

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2} .4610009{col 26}{space 2}  .147106{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .1724422{col 67}{space 3} .7495595
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.642447{col 26}{space 2} .0766069{col 37}{space 1}   47.55{col 46}{space 3}0.000{col 54}{space 4} 3.492178{col 67}{space 3} 3.792717
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(45,418 missing values generated)
(45,418 missing values generated)

Resampling replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}{p2colset 7 17 21 2}{...}

{txt}{p2col :command:}regress euview1 treatment if cntry=="ES", robust{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000001
     {txt}Clusters{res}:  1668
{txt}Strata var(s){res}:  none
       {txt}Strata{res}:  1

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .4610009{col 27}      3{col 35}   1000{col 43} 0.0030{col 51} 0.0017{col 59} .0006191{col 69}{space 1}  .008742
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}
{com}. gr save permutation.gph, replace
{res}{txt}(file permutation.gph saved)

{com}. 
. ritest treatment _b[treatment], reps(1000) kdensityplot: reg diswd treatment if cntry=="ES", robust
{res}{txt}(running {bf:regress} on estimation sample)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(1, 1582)        =  {res}     5.81
                                                {txt}Prob > F          = {res}    0.0161
                                                {txt}R-squared         = {res}    0.0038
                                                {txt}Root MSE          =    {res} 2.4866

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2} .3368017{col 26}{space 2} .1397341{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0627183{col 67}{space 3} .6108852
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}    5.112{col 26}{space 2} .0733777{col 37}{space 1}   69.67{col 46}{space 3}0.000{col 54}{space 4} 4.968072{col 67}{space 3} 5.255928
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(45,418 missing values generated)
(45,418 missing values generated)

Resampling replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}{p2colset 7 17 21 2}{...}

{txt}{p2col :command:}regress diswd treatment if cntry=="ES", robust{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000001
     {txt}Clusters{res}:  1668
{txt}Strata var(s){res}:  none
       {txt}Strata{res}:  1

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .3368017{col 27}      6{col 35}   1000{col 43} 0.0060{col 51} 0.0024{col 59}  .002205{col 69}{space 1} .0130134
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}
{com}. gr save permutation0.gph, replace
{res}{txt}(file permutation0.gph saved)

{com}. 
. ritest treatment _b[treatment], reps(1000) kdensityplot: reg trustlegal treatment if cntry=="ES", robust
{res}{txt}(running {bf:regress} on estimation sample)

Linear regression                               Number of obs     = {res}     1,635
                                                {txt}F(1, 1633)        =  {res}     3.77
                                                {txt}Prob > F          = {res}    0.0523
                                                {txt}R-squared         = {res}    0.0024
                                                {txt}Root MSE          =    {res} 2.6697

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  trustlegal{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2} .2854643{col 26}{space 2}   .14698{col 37}{space 1}    1.94{col 46}{space 3}0.052{col 54}{space 4}-.0028248{col 67}{space 3} .5737534
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.134367{col 26}{space 2} .0778013{col 37}{space 1}   65.99{col 46}{space 3}0.000{col 54}{space 4} 4.981766{col 67}{space 3} 5.286968
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(45,418 missing values generated)
(45,418 missing values generated)

Resampling replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}{p2colset 7 17 21 2}{...}

{txt}{p2col :command:}regress trustlegal treatment if cntry=="ES", robust{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000001
     {txt}Clusters{res}:  1668
{txt}Strata var(s){res}:  none
       {txt}Strata{res}:  1

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} .2854643{col 27}     40{col 35}   1000{col 43} 0.0400{col 51} 0.0062{col 59} .0287276{col 69}{space 1} .0540727
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}
{com}. gr save permutation1.gph, replace
{res}{txt}(file permutation1.gph saved)

{com}. 
. ritest treatment _b[treatment], reps(1000) kdensityplot: reg trstun1 treatment if cntry=="ES", robust
{res}{txt}(running {bf:regress} on estimation sample)

Linear regression                               Number of obs     = {res}     1,446
                                                {txt}F(1, 1444)        =  {res}     0.34
                                                {txt}Prob > F          = {res}    0.5584
                                                {txt}R-squared         = {res}    0.0002
                                                {txt}Root MSE          =    {res}  2.565

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     trstun1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0867844{col 26}{space 2}  .148263{col 37}{space 1}   -0.59{col 46}{space 3}0.558{col 54}{space 4}-.3776183{col 67}{space 3} .2040496
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.023553{col 26}{space 2} .0801994{col 37}{space 1}   62.64{col 46}{space 3}0.000{col 54}{space 4} 4.866233{col 67}{space 3} 5.180872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(45,418 missing values generated)
(45,418 missing values generated)

Resampling replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}{p2colset 7 17 21 2}{...}

{txt}{p2col :command:}regress trstun1 treatment if cntry=="ES", robust{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000001
     {txt}Clusters{res}:  1668
{txt}Strata var(s){res}:  none
       {txt}Strata{res}:  1

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.0867844{col 27}    550{col 35}   1000{col 43} 0.5500{col 51} 0.0157{col 59} .5185565{col 69}{space 1} .5811483
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}{txt}
{com}. gr save permutation3.gph, replace
{res}{txt}(file permutation3.gph saved)

{com}. 
. gr combine permutation.gph permutation0.gph permutation1.gph permutation3.gph , title("Permutation tests") col(2)
{res}{txt}
{com}. gr save permutation_combined1.gph, replace
{res}{txt}(file permutation_combined1.gph saved)

{com}. 
. 
. 
. //Reg tables for appendix//
. reg euview1 i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,483.03945732117)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(1, 1479)        =  {res}    10.36
                                                {txt}Prob > F          = {res}    0.0013
                                                {txt}R-squared         = {res}    0.0075
                                                {txt}Root MSE          =    {res} 2.5336

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4837643{col 26}{space 2} .1503062{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .1889283{col 67}{space 3} .7786003
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.655697{col 26}{space 2} .0778174{col 37}{space 1}   46.98{col 46}{space 3}0.000{col 54}{space 4} 3.503053{col 67}{space 3} 3.808342
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using regtable.tex, dec(2) replace
{txt}{stata `"shellout using `"regtable.tex"'"':regtable.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable.txt""':seeout}

{com}. reg euview1 i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,483.03945732117)

Linear regression                               Number of obs     = {res}     1,481
                                                {txt}F(14, 1466)       =  {res}     8.17
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0824
                                                {txt}Root MSE          =    {res} 2.4469

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     euview1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4673591{col 26}{space 2} .1468383{col 37}{space 1}    3.18{col 46}{space 3}0.001{col 54}{space 4} .1793236{col 67}{space 3} .7553947
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.3394577{col 26}{space 2} .1299738{col 37}{space 1}   -2.61{col 46}{space 3}0.009{col 54}{space 4}-.5944121{col 67}{space 3}-.0845033
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4515842{col 26}{space 2} .2473679{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0336486{col 67}{space 3}  .936817
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5123816{col 26}{space 2} .2356077{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0502173{col 67}{space 3} .9745458
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3174599{col 26}{space 2} .2254044{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.1246896{col 67}{space 3} .7596094
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1134078{col 26}{space 2} .2310335{col 37}{space 1}    0.49{col 46}{space 3}0.624{col 54}{space 4}-.3397838{col 67}{space 3} .5665993
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2027136{col 26}{space 2} .2602598{col 37}{space 1}    0.78{col 46}{space 3}0.436{col 54}{space 4}-.3078077{col 67}{space 3} .7132349
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .6239109{col 26}{space 2} .2993889{col 37}{space 1}    2.08{col 46}{space 3}0.037{col 54}{space 4} .0366345{col 67}{space 3} 1.211187
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2068665{col 26}{space 2}    .2396{col 37}{space 1}   -0.86{col 46}{space 3}0.388{col 54}{space 4} -.676862{col 67}{space 3}  .263129
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5576301{col 26}{space 2} .2358931{col 37}{space 1}   -2.36{col 46}{space 3}0.018{col 54}{space 4}-1.020354{col 67}{space 3} -.094906
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.9221003{col 26}{space 2} .3059081{col 37}{space 1}   -3.01{col 46}{space 3}0.003{col 54}{space 4}-1.522165{col 67}{space 3} -.322036
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-1.177681{col 26}{space 2} .2372037{col 37}{space 1}   -4.96{col 46}{space 3}0.000{col 54}{space 4}-1.642975{col 67}{space 3}-.7123858
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2}-.0341645{col 26}{space 2} .0327503{col 37}{space 1}   -1.04{col 46}{space 3}0.297{col 54}{space 4} -.098407{col 67}{space 3}  .030078
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2}  .197989{col 26}{space 2} .0344981{col 37}{space 1}    5.74{col 46}{space 3}0.000{col 54}{space 4}  .130318{col 67}{space 3}   .26566
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.395204{col 26}{space 2} .3383878{col 37}{space 1}   10.03{col 46}{space 3}0.000{col 54}{space 4} 2.731428{col 67}{space 3}  4.05898
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using regtable.tex, dec(2) append
{txt}{stata `"shellout using `"regtable.tex"'"':regtable.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable.txt""':seeout}

{com}. reg diswd i.treatment [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,584.48301851749)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(1, 1582)        =  {res}     5.32
                                                {txt}Prob > F          = {res}    0.0212
                                                {txt}R-squared         = {res}    0.0035
                                                {txt}Root MSE          =    {res} 2.4794

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .321891{col 26}{space 2} .1395171{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0482333{col 67}{space 3} .5955488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.123155{col 26}{space 2} .0734326{col 37}{space 1}   69.77{col 46}{space 3}0.000{col 54}{space 4}  4.97912{col 67}{space 3} 5.267191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using regtable.tex, dec(2) append
{txt}{stata `"shellout using `"regtable.tex"'"':regtable.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable.txt""':seeout}

{com}. reg diswd i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust
{txt}(sum of wgt is 1,584.48301851749)

Linear regression                               Number of obs     = {res}     1,584
                                                {txt}F(14, 1569)       =  {res}     3.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0295
                                                {txt}Root MSE          =    {res} 2.4569

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       diswd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .3013953{col 26}{space 2} .1389248{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0288974{col 67}{space 3} .5738932
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.2237634{col 26}{space 2} .1266658{col 37}{space 1}   -1.77{col 46}{space 3}0.077{col 54}{space 4}-.4722155{col 67}{space 3} .0246887
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4876092{col 26}{space 2} .2405177{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0158393{col 67}{space 3}  .959379
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4712224{col 26}{space 2}  .227242{col 37}{space 1}    2.07{col 46}{space 3}0.038{col 54}{space 4} .0254925{col 67}{space 3} .9169523
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0465388{col 26}{space 2} .2166379{col 37}{space 1}    0.21{col 46}{space 3}0.830{col 54}{space 4}-.3783916{col 67}{space 3} .4714692
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0796382{col 26}{space 2} .2202759{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.5117043{col 67}{space 3} .3524279
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1016798{col 26}{space 2} .2414978{col 37}{space 1}   -0.42{col 46}{space 3}0.674{col 54}{space 4}-.5753722{col 67}{space 3} .3720126
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .199697{col 26}{space 2} .2803969{col 37}{space 1}    0.71{col 46}{space 3}0.476{col 54}{space 4} -.350295{col 67}{space 3} .7496891
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3771882{col 26}{space 2} .2116384{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0379357{col 67}{space 3} .7923121
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0796793{col 26}{space 2} .2076846{col 37}{space 1}   -0.38{col 46}{space 3}0.701{col 54}{space 4} -.487048{col 67}{space 3} .3276893
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4253169{col 26}{space 2} .3287269{col 37}{space 1}    1.29{col 46}{space 3}0.196{col 54}{space 4}-.2194734{col 67}{space 3} 1.070107
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1348748{col 26}{space 2} .2136237{col 37}{space 1}    0.63{col 46}{space 3}0.528{col 54}{space 4}-.2841432{col 67}{space 3} .5538928
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} .0467766{col 26}{space 2} .0303072{col 37}{space 1}    1.54{col 46}{space 3}0.123{col 54}{space 4}-.0126703{col 67}{space 3} .1062235
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2}-.0993283{col 26}{space 2} .0320461{col 37}{space 1}   -3.10{col 46}{space 3}0.002{col 54}{space 4} -.162186{col 67}{space 3}-.0364707
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.156416{col 26}{space 2} .3137222{col 37}{space 1}   16.44{col 46}{space 3}0.000{col 54}{space 4} 4.541057{col 67}{space 3} 5.771774
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using regtable.tex, dec(2) append
{txt}{stata `"shellout using `"regtable.tex"'"':regtable.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable.txt""':seeout}

{com}. 
. logit escep1 i.treatment [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-715.94249}  
Iteration 1:{space 3}log pseudolikelihood = {res:-712.45498}  
Iteration 2:{space 3}log pseudolikelihood = {res:-712.43707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-712.43707}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,481
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      6.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0085
{txt}Log pseudolikelihood = {res}-712.43707{txt}{col 49}Pseudo R2{col 67}= {res}    0.0049

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      escep1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .3757574{col 26}{space 2} .1427302{col 37}{space 1}    2.63{col 46}{space 3}0.008{col 54}{space 4} .0960114{col 67}{space 3} .6555035
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.585629{col 26}{space 2} .0831663{col 37}{space 1}  -19.07{col 46}{space 3}0.000{col 54}{space 4}-1.748632{col 67}{space 3}-1.422626
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regtable_binary.tex, dec(2) replace
{txt}{stata `"shellout using `"regtable_binary.tex"'"':regtable_binary.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable_binary.txt""':seeout}

{com}. logit escep1 i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-715.94249}  
Iteration 1:{space 3}log pseudolikelihood = {res: -678.9784}  
Iteration 2:{space 3}log pseudolikelihood = {res:-677.53467}  
Iteration 3:{space 3}log pseudolikelihood = {res:-677.52966}  
Iteration 4:{space 3}log pseudolikelihood = {res:-677.52966}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,481
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}     65.63
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-677.52966{txt}{col 49}Pseudo R2{col 67}= {res}    0.0537

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      escep1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .4095346{col 26}{space 2} .1467409{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .1219276{col 67}{space 3} .6971416
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.0248806{col 26}{space 2} .1408978{col 37}{space 1}   -0.18{col 46}{space 3}0.860{col 54}{space 4}-.3010354{col 67}{space 3} .2512741
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .8438959{col 26}{space 2} .2974902{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .2608259{col 67}{space 3} 1.426966
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6134535{col 26}{space 2} .2804496{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .0637825{col 67}{space 3} 1.163125
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .6249733{col 26}{space 2} .2731928{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0895252{col 67}{space 3} 1.160421
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3556504{col 26}{space 2} .2878176{col 37}{space 1}    1.24{col 46}{space 3}0.217{col 54}{space 4}-.2084617{col 67}{space 3} .9197625
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6382227{col 26}{space 2} .3034233{col 37}{space 1}    2.10{col 46}{space 3}0.035{col 54}{space 4}  .043524{col 67}{space 3} 1.232921
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .8473957{col 26}{space 2}  .326202{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .2080516{col 67}{space 3}  1.48674
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.0965314{col 26}{space 2} .2060482{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.5003783{col 67}{space 3} .3073156
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4229874{col 26}{space 2} .2216495{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.8574125{col 67}{space 3} .0114377
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.7779698{col 26}{space 2} .3391263{col 37}{space 1}   -2.29{col 46}{space 3}0.022{col 54}{space 4}-1.442645{col 67}{space 3}-.1132945
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-1.083323{col 26}{space 2} .2538099{col 37}{space 1}   -4.27{col 46}{space 3}0.000{col 54}{space 4}-1.580782{col 67}{space 3}-.5858651
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} -.035514{col 26}{space 2} .0368136{col 37}{space 1}   -0.96{col 46}{space 3}0.335{col 54}{space 4}-.1076674{col 67}{space 3} .0366394
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2} .1259115{col 26}{space 2}  .035194{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .0569325{col 67}{space 3} .1948905
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.134995{col 26}{space 2} .3708537{col 37}{space 1}   -5.76{col 46}{space 3}0.000{col 54}{space 4}-2.861855{col 67}{space 3}-1.408135
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regtable_binary.tex, dec(2) append
{txt}{stata `"shellout using `"regtable_binary.tex"'"':regtable_binary.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable_binary.txt""':seeout}

{com}. logit dissatisfied i.treatment [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1063.1548}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1060.8562}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1060.8558}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1060.8558}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,584
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      4.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0333
{txt}Log pseudolikelihood = {res}-1060.8558{txt}{col 49}Pseudo R2{col 67}= {res}    0.0022

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfied{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2}  .241286{col 26}{space 2} .1133648{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0190952{col 67}{space 3} .4634768
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4972156{col 26}{space 2} .0619604{col 37}{space 1}   -8.02{col 46}{space 3}0.000{col 54}{space 4}-.6186557{col 67}{space 3}-.3757756
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regtable_binary.tex, dec(2) append
{txt}{stata `"shellout using `"regtable_binary.tex"'"':regtable_binary.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable_binary.txt""':seeout}

{com}. logit dissatisfied i.treatment i.gender i.agecat i.education1 c.income1 c.lrscale1 [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1063.1548}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1036.1887}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1036.1157}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1036.1157}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,584
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}     50.49
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1036.1157{txt}{col 49}Pseudo R2{col 67}= {res}    0.0254

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfied{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.treatment {c |}{col 14}{res}{space 2} .2377994{col 26}{space 2} .1168194{col 37}{space 1}    2.04{col 46}{space 3}0.042{col 54}{space 4} .0088375{col 67}{space 3} .4667613
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 7}Male  {c |}{col 14}{res}{space 2}-.1640516{col 26}{space 2} .1069507{col 37}{space 1}   -1.53{col 46}{space 3}0.125{col 54}{space 4}-.3736712{col 67}{space 3}  .045568
{txt}{space 12} {c |}
{space 6}agecat {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4948253{col 26}{space 2} .2118694{col 37}{space 1}    2.34{col 46}{space 3}0.020{col 54}{space 4}  .079569{col 67}{space 3} .9100817
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3561565{col 26}{space 2} .1960263{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0280479{col 67}{space 3} .7403609
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0725169{col 26}{space 2} .1937496{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.4522591{col 67}{space 3} .3072252
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0268763{col 26}{space 2} .2012114{col 37}{space 1}   -0.13{col 46}{space 3}0.894{col 54}{space 4}-.4212433{col 67}{space 3} .3674907
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0816712{col 26}{space 2} .2227435{col 37}{space 1}    0.37{col 46}{space 3}0.714{col 54}{space 4}-.3548981{col 67}{space 3} .5182405
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1728131{col 26}{space 2} .2453533{col 37}{space 1}    0.70{col 46}{space 3}0.481{col 54}{space 4}-.3080705{col 67}{space 3} .6536967
{txt}{space 12} {c |}
{space 2}education1 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .4809008{col 26}{space 2} .1847601{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .1187777{col 67}{space 3} .8430239
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .228298{col 26}{space 2} .1852988{col 37}{space 1}    1.23{col 46}{space 3}0.218{col 54}{space 4}-.1348811{col 67}{space 3}  .591477
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4650678{col 26}{space 2} .2679641{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0601323{col 67}{space 3} .9902678
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4481256{col 26}{space 2} .1886632{col 37}{space 1}    2.38{col 46}{space 3}0.018{col 54}{space 4} .0783524{col 67}{space 3} .8178987
{txt}{space 12} {c |}
{space 5}income1 {c |}{col 14}{res}{space 2} .0378962{col 26}{space 2} .0262028{col 37}{space 1}    1.45{col 46}{space 3}0.148{col 54}{space 4}-.0134604{col 67}{space 3} .0892528
{txt}{space 4}lrscale1 {c |}{col 14}{res}{space 2} -.090632{col 26}{space 2} .0264598{col 37}{space 1}   -3.43{col 46}{space 3}0.001{col 54}{space 4}-.1424922{col 67}{space 3}-.0387718
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6815835{col 26}{space 2} .2803286{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-1.231017{col 67}{space 3}-.1321495
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regtable_binary.tex, dec(2) append
{txt}{stata `"shellout using `"regtable_binary.tex"'"':regtable_binary.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "regtable_binary.txt""':seeout}

{com}. 
. 
. //balance test//
. 
. 
. gen running=0 if inwmms==12 & inwdds==19
{txt}(46,876 missing values generated)

{com}. replace running=1 if inwmms==12 & inwdds==20
{txt}(188 real changes made)

{com}. replace running=2 if inwmms==12 & inwdds==21
{txt}(138 real changes made)

{com}. replace running=3 if inwmms==12 & inwdds==22
{txt}(103 real changes made)

{com}. replace running=4 if inwmms==12 & inwdds==23
{txt}(74 real changes made)

{com}. replace running=5 if inwmms==12 & inwdds==24
{txt}(19 real changes made)

{com}. replace running=6 if inwmms==12 & inwdds==25
{txt}(20 real changes made)

{com}. replace running=7 if inwmms==12 & inwdds==26
{txt}(40 real changes made)

{com}. replace running=8 if inwmms==12 & inwdds==27
{txt}(134 real changes made)

{com}. replace running=9 if inwmms==12 & inwdds==28
{txt}(163 real changes made)

{com}. replace running=10 if inwmms==12 & inwdds==30
{txt}(78 real changes made)

{com}. replace running=11 if inwmms==12 & inwdds==31
{txt}(28 real changes made)

{com}. replace running=12 if inwmms==1 & inwdds==1
{txt}(6 real changes made)

{com}. replace running=13 if inwmms==1 & inwdds==2
{txt}(107 real changes made)

{com}. replace running=14 if inwmms==1 & inwdds==3
{txt}(171 real changes made)

{com}. replace running=15 if inwmms==1 & inwdds==4
{txt}(219 real changes made)

{com}. replace running=16 if inwmms==1 & inwdds==5
{txt}(205 real changes made)

{com}. replace running=17 if inwmms==1 & inwdds==6
{txt}(134 real changes made)

{com}. replace running=18 if inwmms==1 & inwdds==7
{txt}(289 real changes made)

{com}. replace running=19 if inwmms==1 & inwdds==8
{txt}(321 real changes made)

{com}. replace running=20 if inwmms==1 & inwdds==9
{txt}(299 real changes made)

{com}. replace running=21 if inwmms==1 & inwdds==10
{txt}(322 real changes made)

{com}. replace running=22 if inwmms==1 & inwdds==11
{txt}(273 real changes made)

{com}. replace running=23 if inwmms==1 & inwdds==12
{txt}(272 real changes made)

{com}. replace running=24 if inwmms==1 & inwdds==13
{txt}(173 real changes made)

{com}. replace running=25 if inwmms==1 & inwdds==14
{txt}(243 real changes made)

{com}. replace running=26 if inwmms==1 & inwdds==15
{txt}(269 real changes made)

{com}. replace running=27 if inwmms==1 & inwdds==16
{txt}(269 real changes made)

{com}. replace running=28 if inwmms==1 & inwdds==17
{txt}(259 real changes made)

{com}. replace running=29 if inwmms==1 & inwdds==18
{txt}(238 real changes made)

{com}. replace running=30 if inwmms==1 & inwdds==19
{txt}(228 real changes made)

{com}. replace running=31 if inwmms==1 & inwdds==20
{txt}(80 real changes made)

{com}. replace running=32 if inwmms==1 & inwdds==21
{txt}(197 real changes made)

{com}. replace running=33 if inwmms==1 & inwdds==22
{txt}(222 real changes made)

{com}. replace running=34 if inwmms==1 & inwdds==23
{txt}(162 real changes made)

{com}. replace running=35 if inwmms==1 & inwdds==24
{txt}(203 real changes made)

{com}. replace running=36 if inwmms==1 & inwdds==25
{txt}(155 real changes made)

{com}. replace running=37 if inwmms==1 & inwdds==26
{txt}(151 real changes made)

{com}. replace running=38 if inwmms==1 & inwdds==27
{txt}(55 real changes made)

{com}. 
. 
. replace running=-1 if inwmms==12 & inwdds==18
{txt}(248 real changes made)

{com}. replace running=-2 if inwmms==12 & inwdds==17
{txt}(243 real changes made)

{com}. replace running=-3 if inwmms==12 & inwdds==16
{txt}(139 real changes made)

{com}. replace running=-4 if inwmms==12 & inwdds==15
{txt}(199 real changes made)

{com}. replace running=-5 if inwmms==12 & inwdds==14
{txt}(261 real changes made)

{com}. replace running=-6 if inwmms==12 & inwdds==13
{txt}(295 real changes made)

{com}. replace running=-7 if inwmms==12 & inwdds==12
{txt}(335 real changes made)

{com}. replace running=-8 if inwmms==12 & inwdds==11
{txt}(313 real changes made)

{com}. replace running=-9 if inwmms==12 & inwdds==10
{txt}(335 real changes made)

{com}. replace running=-10 if inwmms==12 & inwdds==9
{txt}(178 real changes made)

{com}. replace running=-11 if inwmms==12 & inwdds==8
{txt}(242 real changes made)

{com}. replace running=-12 if inwmms==12 & inwdds==7
{txt}(272 real changes made)

{com}. replace running=-13 if inwmms==12 & inwdds==6
{txt}(289 real changes made)

{com}. replace running=-14 if inwmms==12 & inwdds==5
{txt}(362 real changes made)

{com}. replace running=-15 if inwmms==12 & inwdds==4
{txt}(389 real changes made)

{com}. replace running=-16 if inwmms==12 & inwdds==3
{txt}(411 real changes made)

{com}. replace running=-17 if inwmms==12 & inwdds==2
{txt}(219 real changes made)

{com}. replace running=-18 if inwmms==12 & inwdds==1
{txt}(345 real changes made)

{com}. 
. replace running=-19 if inwmms==11 & inwdds==30
{txt}(376 real changes made)

{com}. replace running=-20 if inwmms==11 & inwdds==29
{txt}(403 real changes made)

{com}. replace running=-21 if inwmms==11 & inwdds==28
{txt}(464 real changes made)

{com}. replace running=-22 if inwmms==11 & inwdds==27
{txt}(448 real changes made)

{com}. replace running=-23 if inwmms==11 & inwdds==26
{txt}(438 real changes made)

{com}. replace running=-24 if inwmms==11 & inwdds==25
{txt}(268 real changes made)

{com}. replace running=-25 if inwmms==11 & inwdds==24
{txt}(366 real changes made)

{com}. replace running=-26 if inwmms==11 & inwdds==23
{txt}(423 real changes made)

{com}. replace running=-27 if inwmms==11 & inwdds==22
{txt}(386 real changes made)

{com}. replace running=-28 if inwmms==11 & inwdds==21
{txt}(365 real changes made)

{com}. replace running=-29 if inwmms==11 & inwdds==19
{txt}(413 real changes made)

{com}. replace running=-30 if inwmms==11 & inwdds==18
{txt}(238 real changes made)

{com}. replace running=-31 if inwmms==11 & inwdds==17
{txt}(299 real changes made)

{com}. replace running=-32 if inwmms==11 & inwdds==16
{txt}(370 real changes made)

{com}. replace running=-33 if inwmms==11 & inwdds==15
{txt}(366 real changes made)

{com}. replace running=-34 if inwmms==11 & inwdds==14
{txt}(377 real changes made)

{com}. replace running=-35 if inwmms==11 & inwdds==13
{txt}(370 real changes made)

{com}. replace running=-36 if inwmms==11 & inwdds==12
{txt}(384 real changes made)

{com}. replace running=-37 if inwmms==11 & inwdds==11
{txt}(180 real changes made)

{com}. 
. logit treatment i.gender i.agecat i.education c.incomenew c.lrscale [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-616.30218}  
Iteration 1:{space 3}log pseudolikelihood = {res: -603.4479}  
Iteration 2:{space 3}log pseudolikelihood = {res:-603.22997}  
Iteration 3:{space 3}log pseudolikelihood = {res:-603.22965}  
Iteration 4:{space 3}log pseudolikelihood = {res:-603.22965}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,031
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     23.09
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0406
{txt}Log pseudolikelihood = {res}-603.22965{txt}{col 49}Pseudo R2{col 67}= {res}    0.0212

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}       treatment{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gender {c |}
{space 11}Male  {c |}{col 18}{res}{space 2} .0452646{col 30}{space 2} .1439181{col 41}{space 1}    0.31{col 50}{space 3}0.753{col 58}{space 4}-.2368097{col 71}{space 3} .3273389
{txt}{space 16} {c |}
{space 10}agecat {c |}
{space 14}1  {c |}{col 18}{res}{space 2} .3582487{col 30}{space 2} .2863072{col 41}{space 1}    1.25{col 50}{space 3}0.211{col 58}{space 4}-.2029031{col 71}{space 3} .9194006
{txt}{space 14}2  {c |}{col 18}{res}{space 2}   .52737{col 30}{space 2} .2673883{col 41}{space 1}    1.97{col 50}{space 3}0.049{col 58}{space 4} .0032986{col 71}{space 3} 1.051441
{txt}{space 14}3  {c |}{col 18}{res}{space 2} .2359398{col 30}{space 2} .2644757{col 41}{space 1}    0.89{col 50}{space 3}0.372{col 58}{space 4} -.282423{col 71}{space 3} .7543025
{txt}{space 14}4  {c |}{col 18}{res}{space 2}-.0063729{col 30}{space 2} .2837197{col 41}{space 1}   -0.02{col 50}{space 3}0.982{col 58}{space 4}-.5624534{col 71}{space 3} .5497076
{txt}{space 14}5  {c |}{col 18}{res}{space 2}-.5193155{col 30}{space 2} .3493683{col 41}{space 1}   -1.49{col 50}{space 3}0.137{col 58}{space 4}-1.204065{col 71}{space 3} .1654338
{txt}{space 14}6  {c |}{col 18}{res}{space 2}-.5761423{col 30}{space 2} .4377612{col 41}{space 1}   -1.32{col 50}{space 3}0.188{col 58}{space 4}-1.434138{col 71}{space 3}  .281854
{txt}{space 16} {c |}
{space 7}education {c |}
Lower secondary  {c |}{col 18}{res}{space 2} .1432027{col 30}{space 2} .2514856{col 41}{space 1}    0.57{col 50}{space 3}0.569{col 58}{space 4}   -.3497{col 71}{space 3} .6361055
{txt}Upper secondary  {c |}{col 18}{res}{space 2} .2717605{col 30}{space 2} .2725158{col 41}{space 1}    1.00{col 50}{space 3}0.319{col 58}{space 4}-.2623606{col 71}{space 3} .8058816
{txt}{space 1}Post-secondary  {c |}{col 18}{res}{space 2} .2490842{col 30}{space 2}   .35324{col 41}{space 1}    0.71{col 50}{space 3}0.481{col 58}{space 4}-.4432536{col 71}{space 3} .9414219
{txt}{space 7}Tertiary  {c |}{col 18}{res}{space 2} .1409658{col 30}{space 2} .2579189{col 41}{space 1}    0.55{col 50}{space 3}0.585{col 58}{space 4} -.364546{col 71}{space 3} .6464776
{txt}{space 16} {c |}
{space 7}incomenew {c |}{col 18}{res}{space 2}-.0172018{col 30}{space 2} .0301677{col 41}{space 1}   -0.57{col 50}{space 3}0.569{col 58}{space 4}-.0763293{col 71}{space 3} .0419257
{txt}{space 9}lrscale {c |}{col 18}{res}{space 2}-.0001846{col 30}{space 2} .0304692{col 41}{space 1}   -0.01{col 50}{space 3}0.995{col 58}{space 4}-.0599031{col 71}{space 3}  .059534
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -1.14318{col 30}{space 2}  .366054{col 41}{space 1}   -3.12{col 50}{space 3}0.002{col 58}{space 4}-1.860633{col 71}{space 3}-.4257276
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using balancetest1.tex, dec(2) replace
{txt}{stata `"shellout using `"balancetest1.tex"'"':balancetest1.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "balancetest1.txt""':seeout}

{com}. logit treatment i.gender i.agecat i.education c.incomenew c.lrscale [pweight=dweight] if cntry=="ES" & running>-10 & running<10, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-141.30983}  
Iteration 1:{space 3}log pseudolikelihood = {res:-134.64936}  
Iteration 2:{space 3}log pseudolikelihood = {res:-134.62797}  
Iteration 3:{space 3}log pseudolikelihood = {res:-134.62797}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       201
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     11.47
{txt}{col 49}Prob > chi2{col 67}= {res}    0.5713
{txt}Log pseudolikelihood = {res}-134.62797{txt}{col 49}Pseudo R2{col 67}= {res}    0.0473

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}       treatment{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gender {c |}
{space 11}Male  {c |}{col 18}{res}{space 2} .6195065{col 30}{space 2} .3020777{col 41}{space 1}    2.05{col 50}{space 3}0.040{col 58}{space 4} .0274451{col 71}{space 3} 1.211568
{txt}{space 16} {c |}
{space 10}agecat {c |}
{space 14}1  {c |}{col 18}{res}{space 2}-.2352625{col 30}{space 2} .5536205{col 41}{space 1}   -0.42{col 50}{space 3}0.671{col 58}{space 4}-1.320339{col 71}{space 3} .8498137
{txt}{space 14}2  {c |}{col 18}{res}{space 2} .5159372{col 30}{space 2} .5535439{col 41}{space 1}    0.93{col 50}{space 3}0.351{col 58}{space 4}-.5689888{col 71}{space 3} 1.600863
{txt}{space 14}3  {c |}{col 18}{res}{space 2} .3276471{col 30}{space 2} .5422578{col 41}{space 1}    0.60{col 50}{space 3}0.546{col 58}{space 4}-.7351586{col 71}{space 3} 1.390453
{txt}{space 14}4  {c |}{col 18}{res}{space 2} .5400761{col 30}{space 2} .5822084{col 41}{space 1}    0.93{col 50}{space 3}0.354{col 58}{space 4}-.6010314{col 71}{space 3} 1.681183
{txt}{space 14}5  {c |}{col 18}{res}{space 2} .5763249{col 30}{space 2} .8039729{col 41}{space 1}    0.72{col 50}{space 3}0.473{col 58}{space 4} -.999433{col 71}{space 3} 2.152083
{txt}{space 14}6  {c |}{col 18}{res}{space 2}-.1616167{col 30}{space 2} .8751939{col 41}{space 1}   -0.18{col 50}{space 3}0.853{col 58}{space 4}-1.876965{col 71}{space 3} 1.553732
{txt}{space 16} {c |}
{space 7}education {c |}
Lower secondary  {c |}{col 18}{res}{space 2} .4295352{col 30}{space 2} .6098013{col 41}{space 1}    0.70{col 50}{space 3}0.481{col 58}{space 4}-.7656534{col 71}{space 3} 1.624724
{txt}Upper secondary  {c |}{col 18}{res}{space 2} .8255376{col 30}{space 2} .6907207{col 41}{space 1}    1.20{col 50}{space 3}0.232{col 58}{space 4}-.5282502{col 71}{space 3} 2.179325
{txt}{space 1}Post-secondary  {c |}{col 18}{res}{space 2} .3364969{col 30}{space 2} .8284767{col 41}{space 1}    0.41{col 50}{space 3}0.685{col 58}{space 4}-1.287288{col 71}{space 3} 1.960281
{txt}{space 7}Tertiary  {c |}{col 18}{res}{space 2} .7434555{col 30}{space 2} .6549302{col 41}{space 1}    1.14{col 50}{space 3}0.256{col 58}{space 4}-.5401841{col 71}{space 3} 2.027095
{txt}{space 16} {c |}
{space 7}incomenew {c |}{col 18}{res}{space 2} .0692219{col 30}{space 2} .0652064{col 41}{space 1}    1.06{col 50}{space 3}0.288{col 58}{space 4}-.0585803{col 71}{space 3} .1970242
{txt}{space 9}lrscale {c |}{col 18}{res}{space 2} .0606115{col 30}{space 2} .0696152{col 41}{space 1}    0.87{col 50}{space 3}0.384{col 58}{space 4}-.0758317{col 71}{space 3} .1970548
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.745767{col 30}{space 2} .8400154{col 41}{space 1}   -2.08{col 50}{space 3}0.038{col 58}{space 4}-3.392167{col 71}{space 3}-.0993667
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using balancetest1.tex, dec(2) append
{txt}{stata `"shellout using `"balancetest1.tex"'"':balancetest1.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "balancetest1.txt""':seeout}

{com}. logit treatment i.gender i.agecat i.education c.incomenew c.lrscale [pweight=dweight] if cntry=="ES" & running>-15 & running<15, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-193.20459}  
Iteration 1:{space 3}log pseudolikelihood = {res:-187.10304}  
Iteration 2:{space 3}log pseudolikelihood = {res:-187.09413}  
Iteration 3:{space 3}log pseudolikelihood = {res:-187.09413}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       275
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     11.55
{txt}{col 49}Prob > chi2{col 67}= {res}    0.5648
{txt}Log pseudolikelihood = {res}-187.09413{txt}{col 49}Pseudo R2{col 67}= {res}    0.0316

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}       treatment{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gender {c |}
{space 11}Male  {c |}{col 18}{res}{space 2} .4939096{col 30}{space 2} .2537313{col 41}{space 1}    1.95{col 50}{space 3}0.052{col 58}{space 4}-.0033946{col 71}{space 3} .9912137
{txt}{space 16} {c |}
{space 10}agecat {c |}
{space 14}1  {c |}{col 18}{res}{space 2} .3593458{col 30}{space 2} .4708749{col 41}{space 1}    0.76{col 50}{space 3}0.445{col 58}{space 4} -.563552{col 71}{space 3} 1.282244
{txt}{space 14}2  {c |}{col 18}{res}{space 2} .8723141{col 30}{space 2} .4651388{col 41}{space 1}    1.88{col 50}{space 3}0.061{col 58}{space 4}-.0393412{col 71}{space 3} 1.783969
{txt}{space 14}3  {c |}{col 18}{res}{space 2} .3120537{col 30}{space 2}  .454338{col 41}{space 1}    0.69{col 50}{space 3}0.492{col 58}{space 4}-.5784324{col 71}{space 3}  1.20254
{txt}{space 14}4  {c |}{col 18}{res}{space 2} .4845474{col 30}{space 2} .4935518{col 41}{space 1}    0.98{col 50}{space 3}0.326{col 58}{space 4}-.4827964{col 71}{space 3} 1.451891
{txt}{space 14}5  {c |}{col 18}{res}{space 2} .3719265{col 30}{space 2} .6529964{col 41}{space 1}    0.57{col 50}{space 3}0.569{col 58}{space 4}-.9079228{col 71}{space 3} 1.651776
{txt}{space 14}6  {c |}{col 18}{res}{space 2}-.1576732{col 30}{space 2} .7483366{col 41}{space 1}   -0.21{col 50}{space 3}0.833{col 58}{space 4}-1.624386{col 71}{space 3}  1.30904
{txt}{space 16} {c |}
{space 7}education {c |}
Lower secondary  {c |}{col 18}{res}{space 2}-.0873049{col 30}{space 2} .4916052{col 41}{space 1}   -0.18{col 50}{space 3}0.859{col 58}{space 4}-1.050833{col 71}{space 3} .8762236
{txt}Upper secondary  {c |}{col 18}{res}{space 2} .0527419{col 30}{space 2} .5531367{col 41}{space 1}    0.10{col 50}{space 3}0.924{col 58}{space 4}-1.031386{col 71}{space 3}  1.13687
{txt}{space 1}Post-secondary  {c |}{col 18}{res}{space 2}-.2367864{col 30}{space 2} .6788328{col 41}{space 1}   -0.35{col 50}{space 3}0.727{col 58}{space 4}-1.567274{col 71}{space 3} 1.093701
{txt}{space 7}Tertiary  {c |}{col 18}{res}{space 2}-.0286196{col 30}{space 2}   .51795{col 41}{space 1}   -0.06{col 50}{space 3}0.956{col 58}{space 4}-1.043783{col 71}{space 3} .9865437
{txt}{space 16} {c |}
{space 7}incomenew {c |}{col 18}{res}{space 2} .0603844{col 30}{space 2} .0550727{col 41}{space 1}    1.10{col 50}{space 3}0.273{col 58}{space 4} -.047556{col 71}{space 3} .1683249
{txt}{space 9}lrscale {c |}{col 18}{res}{space 2} .0524218{col 30}{space 2} .0620087{col 41}{space 1}    0.85{col 50}{space 3}0.398{col 58}{space 4}-.0691131{col 71}{space 3} .1739566
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.219222{col 30}{space 2} .6692658{col 41}{space 1}   -1.82{col 50}{space 3}0.068{col 58}{space 4}-2.530959{col 71}{space 3} .0925147
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using balancetest1.tex, dec(2) append
{txt}{stata `"shellout using `"balancetest1.tex"'"':balancetest1.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "balancetest1.txt""':seeout}

{com}. logit treatment i.gender i.agecat i.education c.incomenew c.lrscale [pweight=dweight] if cntry=="ES" & running>-20 & running<20, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-274.91197}  
Iteration 1:{space 3}log pseudolikelihood = {res:-269.68339}  
Iteration 2:{space 3}log pseudolikelihood = {res:-269.67554}  
Iteration 3:{space 3}log pseudolikelihood = {res:-269.67554}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       398
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}      9.95
{txt}{col 49}Prob > chi2{col 67}= {res}    0.6976
{txt}Log pseudolikelihood = {res}-269.67554{txt}{col 49}Pseudo R2{col 67}= {res}    0.0190

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}       treatment{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gender {c |}
{space 11}Male  {c |}{col 18}{res}{space 2} .2195139{col 30}{space 2} .2106769{col 41}{space 1}    1.04{col 50}{space 3}0.297{col 58}{space 4}-.1934053{col 71}{space 3}  .632433
{txt}{space 16} {c |}
{space 10}agecat {c |}
{space 14}1  {c |}{col 18}{res}{space 2}  .214899{col 30}{space 2} .4080965{col 41}{space 1}    0.53{col 50}{space 3}0.598{col 58}{space 4}-.5849555{col 71}{space 3} 1.014753
{txt}{space 14}2  {c |}{col 18}{res}{space 2} .5060502{col 30}{space 2} .3998364{col 41}{space 1}    1.27{col 50}{space 3}0.206{col 58}{space 4}-.2776148{col 71}{space 3} 1.289715
{txt}{space 14}3  {c |}{col 18}{res}{space 2} .0615493{col 30}{space 2} .3971096{col 41}{space 1}    0.15{col 50}{space 3}0.877{col 58}{space 4}-.7167712{col 71}{space 3} .8398699
{txt}{space 14}4  {c |}{col 18}{res}{space 2} .0504764{col 30}{space 2} .4179218{col 41}{space 1}    0.12{col 50}{space 3}0.904{col 58}{space 4}-.7686352{col 71}{space 3} .8695881
{txt}{space 14}5  {c |}{col 18}{res}{space 2}-.0511117{col 30}{space 2} .5370579{col 41}{space 1}   -0.10{col 50}{space 3}0.924{col 58}{space 4}-1.103726{col 71}{space 3} 1.001503
{txt}{space 14}6  {c |}{col 18}{res}{space 2}-.5700983{col 30}{space 2} .6176577{col 41}{space 1}   -0.92{col 50}{space 3}0.356{col 58}{space 4}-1.780685{col 71}{space 3} .6404886
{txt}{space 16} {c |}
{space 7}education {c |}
Lower secondary  {c |}{col 18}{res}{space 2}-.1485018{col 30}{space 2} .3993087{col 41}{space 1}   -0.37{col 50}{space 3}0.710{col 58}{space 4}-.9311326{col 71}{space 3} .6341289
{txt}Upper secondary  {c |}{col 18}{res}{space 2} .1702284{col 30}{space 2} .4483502{col 41}{space 1}    0.38{col 50}{space 3}0.704{col 58}{space 4}-.7085219{col 71}{space 3} 1.048979
{txt}{space 1}Post-secondary  {c |}{col 18}{res}{space 2} .1355572{col 30}{space 2} .5763041{col 41}{space 1}    0.24{col 50}{space 3}0.814{col 58}{space 4} -.993978{col 71}{space 3} 1.265092
{txt}{space 7}Tertiary  {c |}{col 18}{res}{space 2}-.2003754{col 30}{space 2} .4210447{col 41}{space 1}   -0.48{col 50}{space 3}0.634{col 58}{space 4}-1.025608{col 71}{space 3}  .624857
{txt}{space 16} {c |}
{space 7}incomenew {c |}{col 18}{res}{space 2} .0218434{col 30}{space 2} .0456333{col 41}{space 1}    0.48{col 50}{space 3}0.632{col 58}{space 4}-.0675962{col 71}{space 3} .1112831
{txt}{space 9}lrscale {c |}{col 18}{res}{space 2} .0586309{col 30}{space 2} .0491928{col 41}{space 1}    1.19{col 50}{space 3}0.233{col 58}{space 4}-.0377852{col 71}{space 3} .1550471
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.7905389{col 30}{space 2} .5433282{col 41}{space 1}   -1.45{col 50}{space 3}0.146{col 58}{space 4}-1.855443{col 71}{space 3} .2743649
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using balancetest1.tex, dec(2) append
{txt}{stata `"shellout using `"balancetest1.tex"'"':balancetest1.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "balancetest1.txt""':seeout}

{com}. 
. 
. replace votespain=6 if votespain==. & cntry=="ES"
{txt}(860 real changes made)

{com}. logit treatment i.votespain [pweight=dweight] if cntry=="ES", robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1003.5796}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1002.1374}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1002.1365}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1002.1365}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,668
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.7287
{txt}Log pseudolikelihood = {res}-1002.1365{txt}{col 49}Pseudo R2{col 67}= {res}    0.0014

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   treatment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}votespain {c |}
{space 7}PSOE  {c |}{col 14}{res}{space 2}-.0045054{col 26}{space 2} .2117618{col 37}{space 1}   -0.02{col 46}{space 3}0.983{col 54}{space 4} -.419551{col 67}{space 3} .4105402
{txt}{space 9}UP  {c |}{col 14}{res}{space 2} .0545113{col 26}{space 2} .2543066{col 37}{space 1}    0.21{col 46}{space 3}0.830{col 54}{space 4}-.4439204{col 67}{space 3} .5529429
{txt}{space 9}Cs  {c |}{col 14}{res}{space 2}  .290571{col 26}{space 2} .3090228{col 37}{space 1}    0.94{col 46}{space 3}0.347{col 54}{space 4}-.3151026{col 67}{space 3} .8962446
{txt}{space 8}VOX  {c |}{col 14}{res}{space 2} .1466045{col 26}{space 2} .2721339{col 37}{space 1}    0.54{col 46}{space 3}0.590{col 54}{space 4}-.3867681{col 67}{space 3} .6799772
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1884884{col 26}{space 2} .1829769{col 37}{space 1}    1.03{col 46}{space 3}0.303{col 54}{space 4}-.1701398{col 67}{space 3} .5471167
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.021512{col 26}{space 2} .1668722{col 37}{space 1}   -6.12{col 46}{space 3}0.000{col 54}{space 4}-1.348576{col 67}{space 3}-.6944485
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using balancetest2.tex, dec(2) replace
{txt}{stata `"shellout using `"balancetest2.tex"'"':balancetest2.tex}
{browse `"/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test"' :dir}{com} : {txt}{stata `"seeout using "balancetest2.txt""':seeout}

{com}. 
. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/stuartturnbulldugarte/OneDrive - University of Southampton/Working papers/SPAIN_UESD/REP_test/Replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}23 Feb 2021, 12:01:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}